##
## Call: glm(formula = elephant_type ~ D1, family = binomial, data = ele_plot)
##
## Coefficients:
## (Intercept) D1
## 2.11717 -0.01268
##
## Degrees of Freedom: 257 Total (i.e. Null); 256 Residual
## (17 observations deleted due to missingness)
## Null Deviance: 222.6
## Residual Deviance: 221.2 AIC: 225.2
| Model 1 | ||
|---|---|---|
| (Intercept) | 2.12*** | |
| (0.40) | ||
| D1 | -0.01 | |
| (0.01) | ||
| AIC | 225.17 | |
| BIC | 232.27 | |
| Log Likelihood | -110.58 | |
| Deviance | 221.17 | |
| Num. obs. | 258 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = elephant_type ~ ppol_num, family = binomial, data = ele_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 2.1423 -0.1157
##
## Degrees of Freedom: 215 Total (i.e. Null); 214 Residual
## (59 observations deleted due to missingness)
## Null Deviance: 181.2
## Residual Deviance: 180.4 AIC: 184.4
| Model 1 | ||
|---|---|---|
| (Intercept) | 2.14*** | |
| (0.48) | ||
| ppol_num | -0.12 | |
| (0.13) | ||
| AIC | 184.38 | |
| BIC | 191.13 | |
| Log Likelihood | -90.19 | |
| Deviance | 180.38 | |
| Num. obs. | 216 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = env_type ~ D1, family = binomial, data = env_plot)
##
## Coefficients:
## (Intercept) D1
## 1.1585702 -0.0007942
##
## Degrees of Freedom: 171 Total (i.e. Null); 170 Residual
## (7 observations deleted due to missingness)
## Null Deviance: 191.2
## Residual Deviance: 191.2 AIC: 195.2
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.16** | |
| (0.45) | ||
| D1 | -0.00 | |
| (0.01) | ||
| AIC | 195.21 | |
| BIC | 201.51 | |
| Log Likelihood | -95.61 | |
| Deviance | 191.21 | |
| Num. obs. | 172 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = env_type ~ ppol_num, family = binomial, data = env_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 1.30692 -0.05042
##
## Degrees of Freedom: 140 Total (i.e. Null); 139 Residual
## (38 observations deleted due to missingness)
## Null Deviance: 155.8
## Residual Deviance: 155.6 AIC: 159.6
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.31** | |
| (0.48) | ||
| ppol_num | -0.05 | |
| (0.14) | ||
| AIC | 159.64 | |
| BIC | 165.53 | |
| Log Likelihood | -77.82 | |
| Deviance | 155.64 | |
| Num. obs. | 141 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = trolley_type ~ D1, family = binomial, data = trolley_plot)
##
## Coefficients:
## (Intercept) D1
## 2.58512 -0.03659
##
## Degrees of Freedom: 89 Total (i.e. Null); 88 Residual
## (6 observations deleted due to missingness)
## Null Deviance: 95.35
## Residual Deviance: 89.42 AIC: 93.42
| Model 1 | ||
|---|---|---|
| (Intercept) | 2.59*** | |
| (0.64) | ||
| D1 | -0.04* | |
| (0.02) | ||
| AIC | 93.42 | |
| BIC | 98.42 | |
| Log Likelihood | -44.71 | |
| Deviance | 89.42 | |
| Num. obs. | 90 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = trolley_type ~ ppol_num, family = binomial, data = trolley_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 0.7612 0.1577
##
## Degrees of Freedom: 74 Total (i.e. Null); 73 Residual
## (21 observations deleted due to missingness)
## Null Deviance: 80.28
## Residual Deviance: 79.49 AIC: 83.49
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.76 | |
| (0.58) | ||
| ppol_num | 0.16 | |
| (0.18) | ||
| AIC | 83.49 | |
| BIC | 88.12 | |
| Log Likelihood | -39.74 | |
| Deviance | 79.49 | |
| Num. obs. | 75 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = reflection_type ~ D1, family = binomial, data = reflection_plot)
##
## Coefficients:
## (Intercept) D1
## 1.005838 -0.003409
##
## Degrees of Freedom: 105 Total (i.e. Null); 104 Residual
## (5 observations deleted due to missingness)
## Null Deviance: 128.1
## Residual Deviance: 128 AIC: 132
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.01* | |
| (0.50) | ||
| D1 | -0.00 | |
| (0.01) | ||
| AIC | 132.04 | |
| BIC | 137.37 | |
| Log Likelihood | -64.02 | |
| Deviance | 128.04 | |
| Num. obs. | 106 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = reflection_type ~ ppol_num, family = binomial,
## data = reflection_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 0.4691 0.1599
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## (31 observations deleted due to missingness)
## Null Deviance: 94.11
## Residual Deviance: 93.21 AIC: 97.21
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.47 | |
| (0.58) | ||
| ppol_num | 0.16 | |
| (0.17) | ||
| AIC | 97.21 | |
| BIC | 101.98 | |
| Log Likelihood | -46.61 | |
| Deviance | 93.21 | |
| Num. obs. | 80 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = posthoc_type ~ D1, family = binomial, data = posthoc_plot)
##
## Coefficients:
## (Intercept) D1
## 1.55012 0.03725
##
## Degrees of Freedom: 63 Total (i.e. Null); 62 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 29.93
## Residual Deviance: 28.85 AIC: 32.85
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.55 | |
| (1.23) | ||
| D1 | 0.04 | |
| (0.04) | ||
| AIC | 32.85 | |
| BIC | 37.16 | |
| Log Likelihood | -14.42 | |
| Deviance | 28.85 | |
| Num. obs. | 64 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = posthoc_type ~ ppol_num, family = binomial, data = posthoc_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 2.54424 0.06768
##
## Degrees of Freedom: 49 Total (i.e. Null); 48 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 22.7
## Residual Deviance: 22.67 AIC: 26.67
| Model 1 | ||
|---|---|---|
| (Intercept) | 2.54 | |
| (1.45) | ||
| ppol_num | 0.07 | |
| (0.44) | ||
| AIC | 26.67 | |
| BIC | 30.50 | |
| Log Likelihood | -11.34 | |
| Deviance | 22.67 | |
| Num. obs. | 50 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = psych_type ~ D1, family = binomial, data = psych_plot)
##
## Coefficients:
## (Intercept) D1
## 3.22935 -0.02344
##
## Degrees of Freedom: 61 Total (i.e. Null); 60 Residual
## (8 observations deleted due to missingness)
## Null Deviance: 34.76
## Residual Deviance: 34.03 AIC: 38.03
| Model 1 | ||
|---|---|---|
| (Intercept) | 3.23** | |
| (1.08) | ||
| D1 | -0.02 | |
| (0.03) | ||
| AIC | 38.03 | |
| BIC | 42.28 | |
| Log Likelihood | -17.01 | |
| Deviance | 34.03 | |
| Num. obs. | 62 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = psych_type ~ ppol_num, family = binomial, data = psych_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 0.8997 0.2857
##
## Degrees of Freedom: 51 Total (i.e. Null); 50 Residual
## (18 observations deleted due to missingness)
## Null Deviance: 41.09
## Residual Deviance: 40.32 AIC: 44.32
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.90 | |
| (1.16) | ||
| ppol_num | 0.29 | |
| (0.34) | ||
| AIC | 44.32 | |
| BIC | 48.22 | |
| Log Likelihood | -20.16 | |
| Deviance | 40.32 | |
| Num. obs. | 52 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = confbias_type ~ D1, family = binomial, data = confbias_plot)
##
## Coefficients:
## (Intercept) D1
## 3.734e+00 8.156e-05
##
## Degrees of Freedom: 42 Total (i.e. Null); 41 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 9.499
## Residual Deviance: 9.499 AIC: 13.5
| Model 1 | ||
|---|---|---|
| (Intercept) | 3.73 | |
| (2.58) | ||
| D1 | 0.00 | |
| (0.06) | ||
| AIC | 13.50 | |
| BIC | 17.02 | |
| Log Likelihood | -4.75 | |
| Deviance | 9.50 | |
| Num. obs. | 43 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
Error in stats::fisher.test(ftab, simulate.p.value = (nrow(ftab) > 2 || : need 2 or more non-zero row marginals
only 1 dislike so this doesn’t work
##
## Call: glm(formula = notnew_type ~ D1, family = binomial, data = notnew_plot)
##
## Coefficients:
## (Intercept) D1
## 0.703483 0.000862
##
## Degrees of Freedom: 36 Total (i.e. Null); 35 Residual
## (4 observations deleted due to missingness)
## Null Deviance: 46.63
## Residual Deviance: 46.62 AIC: 50.62
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.70 | |
| (0.84) | ||
| D1 | 0.00 | |
| (0.02) | ||
| AIC | 50.62 | |
| BIC | 53.85 | |
| Log Likelihood | -23.31 | |
| Deviance | 46.62 | |
| Num. obs. | 37 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = notnew_type ~ ppol_num, family = binomial, data = notnew_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 1.07298 -0.02354
##
## Degrees of Freedom: 29 Total (i.e. Null); 28 Residual
## (11 observations deleted due to missingness)
## Null Deviance: 34.79
## Residual Deviance: 34.79 AIC: 38.79
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.07 | |
| (0.99) | ||
| ppol_num | -0.02 | |
| (0.34) | ||
| AIC | 38.79 | |
| BIC | 41.59 | |
| Log Likelihood | -17.40 | |
| Deviance | 34.79 | |
| Num. obs. | 30 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = examplestype ~ D1, family = binomial, data = examplesplot)
##
## Coefficients:
## (Intercept) D1
## -0.496460 -0.004402
##
## Degrees of Freedom: 66 Total (i.e. Null); 65 Residual
## (4 observations deleted due to missingness)
## Null Deviance: 86.19
## Residual Deviance: 86.1 AIC: 90.1
| Model 1 | ||
|---|---|---|
| (Intercept) | -0.50 | |
| (0.58) | ||
| D1 | -0.00 | |
| (0.02) | ||
| AIC | 90.10 | |
| BIC | 94.51 | |
| Log Likelihood | -43.05 | |
| Deviance | 86.10 | |
| Num. obs. | 67 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = examplestype ~ ppol_num, family = binomial, data = examplesplot)
##
## Coefficients:
## (Intercept) ppol_num
## -1.5439 0.2273
##
## Degrees of Freedom: 57 Total (i.e. Null); 56 Residual
## (13 observations deleted due to missingness)
## Null Deviance: 71.85
## Residual Deviance: 70.05 AIC: 74.05
| Model 1 | ||
|---|---|---|
| (Intercept) | -1.54* | |
| (0.64) | ||
| ppol_num | 0.23 | |
| (0.17) | ||
| AIC | 74.05 | |
| BIC | 78.17 | |
| Log Likelihood | -35.02 | |
| Deviance | 70.05 | |
| Num. obs. | 58 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = questions_type ~ D1, family = binomial, data = questions_plot)
##
## Coefficients:
## (Intercept) D1
## 0.019810 -0.007471
##
## Degrees of Freedom: 37 Total (i.e. Null); 36 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 52.26
## Residual Deviance: 52.16 AIC: 56.16
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.02 | |
| (0.80) | ||
| D1 | -0.01 | |
| (0.02) | ||
| AIC | 56.16 | |
| BIC | 59.43 | |
| Log Likelihood | -26.08 | |
| Deviance | 52.16 | |
| Num. obs. | 38 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = questions_type ~ ppol_num, family = binomial, data = questions_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 1.1605 -0.3304
##
## Degrees of Freedom: 27 Total (i.e. Null); 26 Residual
## (12 observations deleted due to missingness)
## Null Deviance: 38.67
## Residual Deviance: 37.36 AIC: 41.36
| Model 1 | ||
|---|---|---|
| (Intercept) | 1.16 | |
| (1.00) | ||
| ppol_num | -0.33 | |
| (0.30) | ||
| AIC | 41.36 | |
| BIC | 44.03 | |
| Log Likelihood | -18.68 | |
| Deviance | 37.36 | |
| Num. obs. | 28 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = explanation_type ~ D1, family = binomial, data = explanation_plot)
##
## Coefficients:
## (Intercept) D1
## -2.17976 0.03623
##
## Degrees of Freedom: 27 Total (i.e. Null); 26 Residual
## (4 observations deleted due to missingness)
## Null Deviance: 35.16
## Residual Deviance: 32.82 AIC: 36.82
| Model 1 | ||
|---|---|---|
| (Intercept) | -2.18* | |
| (1.08) | ||
| D1 | 0.04 | |
| (0.02) | ||
| AIC | 36.82 | |
| BIC | 39.48 | |
| Log Likelihood | -16.41 | |
| Deviance | 32.82 | |
| Num. obs. | 28 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = explanation_type ~ ppol_num, family = binomial,
## data = explanation_plot)
##
## Coefficients:
## (Intercept) ppol_num
## -0.4910 -0.2131
##
## Degrees of Freedom: 20 Total (i.e. Null); 19 Residual
## (11 observations deleted due to missingness)
## Null Deviance: 23.05
## Residual Deviance: 22.8 AIC: 26.8
| Model 1 | ||
|---|---|---|
| (Intercept) | -0.49 | |
| (1.42) | ||
| ppol_num | -0.21 | |
| (0.43) | ||
| AIC | 26.80 | |
| BIC | 28.89 | |
| Log Likelihood | -11.40 | |
| Deviance | 22.80 | |
| Num. obs. | 21 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = psychexercises_type ~ D1, family = binomial, data = psychexercises_plot)
##
## Coefficients:
## (Intercept) D1
## -1.86366 0.08672
##
## Degrees of Freedom: 15 Total (i.e. Null); 14 Residual
## Null Deviance: 19.87
## Residual Deviance: 16.15 AIC: 20.15
| Model 1 | ||
|---|---|---|
| (Intercept) | -1.86 | |
| (1.73) | ||
| D1 | 0.09 | |
| (0.06) | ||
| AIC | 20.15 | |
| BIC | 21.69 | |
| Log Likelihood | -8.07 | |
| Deviance | 16.15 | |
| Num. obs. | 16 | |
| p < 0.001, p < 0.01, p < 0.05 | ||
##
## Call: glm(formula = psychexercises_type ~ ppol_num, family = binomial,
## data = psychexercises_plot)
##
## Coefficients:
## (Intercept) ppol_num
## 0.79858 -0.05647
##
## Degrees of Freedom: 13 Total (i.e. Null); 12 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 18.25
## Residual Deviance: 18.22 AIC: 22.22
| Model 1 | ||
|---|---|---|
| (Intercept) | 0.80 | |
| (1.47) | ||
| ppol_num | -0.06 | |
| (0.36) | ||
| AIC | 22.22 | |
| BIC | 23.50 | |
| Log Likelihood | -9.11 | |
| Deviance | 18.22 | |
| Num. obs. | 14 | |
| p < 0.001, p < 0.01, p < 0.05 | ||